MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers (2212.07841v2)

Abstract: Pre-trained Transformers (\eg BERT) have been commonly used in existing dense retrieval methods for parameter initialization, and recent studies are exploring more effective pre-training tasks for further improving the quality of dense vectors. Although various novel and effective tasks have been proposed, their different input formats and learning objectives make them hard to be integrated for jointly improving the model performance. In this work, we aim to unify a variety of pre-training tasks into the bottlenecked masked autoencoder manner, and integrate them into a multi-task pre-trained model, namely MASTER. Concretely, MASTER utilizes a shared-encoder multi-decoder architecture that can construct a representation bottleneck to compress the abundant semantic information across tasks into dense vectors. Based on it, we integrate three types of representative pre-training tasks: corrupted passages recovering, related passages recovering and PLMs outputs recovering, to characterize the inner-passage information, inter-passage relations and PLMs knowledge. Extensive experiments have shown that our approach outperforms competitive dense retrieval methods. Our code and data are publicly released in \url{https://github.com/microsoft/SimXNS}.

• The paper demonstrates that employing a multi-task pre-training paradigm via a bottlenecked autoencoder significantly enhances dense retrieval performance.
• It utilizes a shared encoder with multiple decoders to effectively embed rich semantic information into compact dense vectors.
• Extensive evaluations reveal that MASTER outperforms state-of-the-art models on benchmarks such as MS-MARCO, TREC-2019, and TREC-2020.

An Analysis of "MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers"

The paper "MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers" presents a novel pre-training framework designed to enhance the performance of dense retrieval systems. The primary innovation in this work is the integration of a multi-task pre-training paradigm via a bottlenecked masked autoencoder architecture. The authors' goal is to improve the encoding of semantic information into dense vectors, thus enhancing the retrieval efficiency and effectiveness in various downstream tasks.

The research proposes an architecture named MASTER, which stands for Multi-task Autoencoded Semantic Textual Ensembles for Retrieval. This model employs a unified framework that allows the incorporation of several pre-training tasks to achieve better dense representations. The key aspects of MASTER are:

1. Shared-Encoder Multi-Decoder Design: The paper adopts a single encoder coupled with multiple task-specific decoders. This design aims to compress rich semantic information into condensed dense vectors that are beneficial across different tasks.
2. Bottlenecked Architecture: In this setup, the encoder is tasked with producing a compressed representation that is then expanded by the decoders. This approach forces the encoder to embed rich and salient information pertinent to various tasks, thereby improving the quality of dense vectors for retrieval purposes.
3. Implementation of Multiple Pre-training Tasks: The paper explores three chief pre-training tasks—-corrupted passages recovering, related passages recovering, and pre-trained LLMs (PLMs) outputs recovering. By leveraging these diverse tasks, MASTER seeks to capture a wide spectrum of semantic relations and knowledge.
4. Extensive Evaluation: The authors conduct extensive experiments to validate the proposed method's efficacy, demonstrating superior performance over baseline models across multiple datasets. The experiments indicate that embedding diverse semantic relationships via multi-task learning can lead to better fine-tuning outcomes in dense retrieval tasks.

The paper contributes to the field by addressing a critical gap in dense retrieval methods: optimizing pre-training strategies to handle complex and diverse information inherent in natural language processing tasks. The results affirm that using a multi-tasking approach within a bottlenecked architecture can substantially improve retrieval performance over traditional single-task pre-training methods.

Strong Numerical Results and Implications

Quantitatively, MASTER demonstrates a statistically significant improvement over previous state-of-the-art models on several benchmarks. Specifically, it shows enhanced performance in retrieval tasks such as MS-MARCO, TREC-2019, and TREC-2020, among others. These outcomes underscore the potential of MASTER to address complex retrieval tasks that require nuanced understanding and semantic alignment between queries and passages.

Implications for Practical Applications

Practically, the ability to deploy a pre-trained model like MASTER can have significant implications for real-world applications such as search engines, question answering systems, and other information retrieval applications. The adoption of a multi-task framework means that organizations could potentially reduce the complexity and cost of training separate models for different retrieval tasks, achieving efficiency without sacrificing performance.

Theoretical Implications and Future Directions

Theoretically, the paper enriches the discourse around multi-task learning in natural language processing by empirically demonstrating its advantages within dense retrieval systems. Future research could build upon this work by exploring the impact of varying the number and type of pre-training tasks or by integrating other advanced architectures to further enhance the adaptability and performance of dense retrievers across diverse scenarios and datasets.

In conclusion, the research presents a compelling case for the use of multi-task pre-trained bottlenecked masked autoencoders in improving dense retrieval tasks, offering both theoretical and practical contributions to the field of machine learning and information retrieval.